IDEAS home Printed from https://ideas.repec.org/p/bdi/opques/qef_970_25.html
   My bibliography  Save this paper

Timely quarterly indicators of household consumption and disposable income for the Italian regions

Author

Listed:
  • Valter Di Giacinto

    (Bank of Italy)

  • Vincenzo Mariani

    (Bank of Italy)

  • Daniele Ruggeri

    (Bank of Italy)

  • Giuseppe Saporito

    (Bank of Italy)

  • Andrea Sechi

    (Bank of Italy)

  • Giovanni Soggia

    (Bank of Italy)

  • Andrea Venturini

    (Bank of Italy)

  • Antonio Veronico

    (Bank of Italy)

Abstract

This study introduces an innovative econometric methodology for developing timely quarterly indicators of household income and consumption for all Italian regions and autonomous provinces. Leveraging a comprehensive statistical database spanning from 1995 to 2022, the methodology utilizes basic indicators from the real economy and monetary sectors. These indicators are condensed into regional common factors and combined with national aggregates to model the annual regional time series published by Istat. Two selection approaches, Stepwise Forward Selection (SFS) and sparse Temporal Disaggregation (spTD) using LASSO-type methods identify relevant local factors. These models interpolate observed annual figures ex-post and provide ex-ante estimates of quarterly regional aggregates with a 90-day post-quarter-end lag. In-sample evaluations show high model fit, particularly with the spTD methodology. Out-of-sample forecasting confirms satisfactory predictive performance, albeit with varying precision across regions. Overall, this methodology yields reliable indicators of regional household income and consumption dynamics, offering timely insights that are crucial for short-term economic analysis and answer the challenges posed by official statistics in Italy, which are annual and released with a one-year lag.

Suggested Citation

  • Valter Di Giacinto & Vincenzo Mariani & Daniele Ruggeri & Giuseppe Saporito & Andrea Sechi & Giovanni Soggia & Andrea Venturini & Antonio Veronico, 2025. "Timely quarterly indicators of household consumption and disposable income for the Italian regions," Questioni di Economia e Finanza (Occasional Papers) 970, Bank of Italy, Economic Research and International Relations Area.
  • Handle: RePEc:bdi:opques:qef_970_25
    as

    Download full text from publisher

    File URL: https://www.bancaditalia.it/pubblicazioni/qef/2025-0970/QEF_970.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    2. Cuevas Ángel & Quilis Enrique M. & Espasa Antoni, 2015. "Quarterly Regional GDP Flash Estimates by Means of Benchmarking and Chain Linking," Journal of Official Statistics, Sciendo, vol. 31(4), pages 627-647, December.
    3. Jushan Bai & Serena Ng, 2004. "A PANIC Attack on Unit Roots and Cointegration," Econometrica, Econometric Society, vol. 72(4), pages 1127-1177, July.
    4. Luke Mosley & Idris Eckley & Alex Gibberd, 2021. "Sparse Temporal Disaggregation," Papers 2108.05783, arXiv.org, revised Oct 2022.
    5. Luke Mosley & Idris A. Eckley & Alex Gibberd, 2022. "Sparse temporal disaggregation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 2203-2233, October.
    6. Chow, Gregory C & Lin, An-loh, 1971. "Best Linear Unbiased Interpolation, Distribution, and Extrapolation of Time Series by Related Series," The Review of Economics and Statistics, MIT Press, vol. 53(4), pages 372-375, November.
    7. Valter Giacinto & Libero Monteforte & Andrea Filippone & Francesco Montaruli & Tiziano Ropele, 2021. "ITER: A Quarterly Indicator of Regional Economic Activity in Italy," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 7(1), pages 129-147, March.
    8. Marta Crispino & Vincenzo Mariani, 2025. "A Tool to Nowcast Tourist Overnight Stays with Payment Data and Complementary Indicators," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 11(1), pages 285-312, March.
    9. Simone Emiliozzi & Concetta Rondinelli & Stefania Villa, 2023. "Consumption during the Covid-19 pandemic: evidence from Italian credit cards," Questioni di Economia e Finanza (Occasional Papers) 769, Bank of Italy, Economic Research and International Relations Area.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Klaus Abberger & Michael Graff & Oliver Müller & Boriss Siliverstovs, 2023. "Imputing Monthly Values for Quarterly Time Series: An Application Performed with Swiss Business Cycle Data," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 19(3), pages 241-273, November.
    2. Kim, Hyun Hak & Swanson, Norman R., 2014. "Forecasting financial and macroeconomic variables using data reduction methods: New empirical evidence," Journal of Econometrics, Elsevier, vol. 178(P2), pages 352-367.
    3. Luke Mosley & Idris A. Eckley & Alex Gibberd, 2022. "Sparse temporal disaggregation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 2203-2233, October.
    4. Valter Giacinto & Libero Monteforte & Andrea Filippone & Francesco Montaruli & Tiziano Ropele, 2021. "ITER: A Quarterly Indicator of Regional Economic Activity in Italy," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 7(1), pages 129-147, March.
    5. Goulet Coulombe, Philippe & Leroux, Maxime & Stevanovic, Dalibor & Surprenant, Stéphane, 2021. "Macroeconomic data transformations matter," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1338-1354.
    6. Mahdi Goldani, 2025. "Daily Forecasting for Annual Time Series Datasets Using Similarity-Based Machine Learning Methods: A Case Study in the Energy Market," Papers 2511.05556, arXiv.org.
    7. Stamer, Vincent, 2024. "Thinking outside the container: A sparse partial least squares approach to forecasting trade flows," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1336-1358.
    8. Luke Mosley & Idris Eckley & Alex Gibberd, 2021. "Sparse Temporal Disaggregation," Papers 2108.05783, arXiv.org, revised Oct 2022.
    9. Atin Aboutorabi & Ga'etan de Rassenfosse, 2024. "Nowcasting R&D Expenditures: A Machine Learning Approach," Papers 2407.11765, arXiv.org.
    10. Hyun Hak Kim & Norman Swanson, 2013. "Mining Big Data Using Parsimonious Factor and Shrinkage Methods," Departmental Working Papers 201316, Rutgers University, Department of Economics.
    11. Gary Koop & Stuart McIntyre & James Mitchell & Aubrey Poon, 2020. "Regional output growth in the United Kingdom: More timely and higher frequency estimates from 1970," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(2), pages 176-197, March.
    12. Tutz, Gerhard & Pößnecker, Wolfgang & Uhlmann, Lorenz, 2015. "Variable selection in general multinomial logit models," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 207-222.
    13. Justin Doran & Bernard Fingleton, 2014. "Economic shocks and growth: Spatio-temporal perspectives on Europe's economies in a time of crisis," Papers in Regional Science, Wiley Blackwell, vol. 93, pages 137-165, November.
    14. Arturas Juodis, 2013. "Cointegration Testing in Panel VAR Models Under Partial Identification and Spatial Dependence," UvA-Econometrics Working Papers 13-08, Universiteit van Amsterdam, Dept. of Econometrics.
    15. João C. Claudio & Katja Heinisch & Oliver Holtemöller, 2020. "Nowcasting East German GDP growth: a MIDAS approach," Empirical Economics, Springer, vol. 58(1), pages 29-54, January.
    16. Nagayasu, Jun, 2010. "Macroeconomic interdependence in East Asia," Japan and the World Economy, Elsevier, vol. 22(4), pages 219-227, December.
    17. Margherita Giuzio, 2017. "Genetic algorithm versus classical methods in sparse index tracking," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 40(1), pages 243-256, November.
    18. Caruso, Alberto & Reichlin, Lucrezia & Ricco, Giovanni, 2019. "Financial and fiscal interaction in the Euro Area crisis: This time was different," European Economic Review, Elsevier, vol. 119(C), pages 333-355.
    19. Xu, Yang & Zhao, Shishun & Hu, Tao & Sun, Jianguo, 2021. "Variable selection for generalized odds rate mixture cure models with interval-censored failure time data," Computational Statistics & Data Analysis, Elsevier, vol. 156(C).
    20. Mariam Camarero & Juan Sapena & Cecilio Tamarit, 2020. "Modelling Time-Varying Parameters in Panel Data State-Space Frameworks: An Application to the Feldstein–Horioka Puzzle," Computational Economics, Springer;Society for Computational Economics, vol. 56(1), pages 87-114, June.

    More about this item

    Keywords

    ;
    ;
    ;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • E01 - Macroeconomics and Monetary Economics - - General - - - Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bdi:opques:qef_970_25. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/bdigvit.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.